How to use the LLM Context Window Visualizer
1. Paste your full prompt (system message + conversation history + new user message) into the text box.
2. Select the target model from the list to see its context window size.
3. Set the tokens you want to reserve for the response (output tokens).
4. The visual bar shows how much of the context window is filled — and how much is left for output.
Tip: token count is estimated (≈ chars ÷ 4). Use the Prompt Token Counter for exact counts.
What is it?
Every LLM has a context window — the maximum number of tokens it can process in a single request. This includes your system message, conversation history, and any documents or context you inject. If your input exceeds the context window, the API rejects the request or the model silently truncates earlier content.
Context windows range from 16k tokens (GPT-3.5) to over 2 million tokens (Gemini 1.5 Pro). Understanding how full your context is helps you avoid truncation, plan for RAG chunk sizes, and optimise prompt lengths for cost.
This tool gives you an instant visual indicator of context usage. You can also reserve a portion of the context window for the output response, since input + max output tokens must both fit within the window limit.
Example
System prompt: 500 tokens Conversation history (5 turns): 2,000 tokens Document context: 10,000 tokens Model: GPT-4o (128k)
Used: 12,500 tokens (9.8%) Remaining for output: 115,500 tokens Status: ✓ Fits comfortably
With Gemini 1.5 Pro (2M context) you could include the entire 1,000-page novel as context.
Benefits
Generate accurate code from your actual data
TypeScript interfaces, Python dataclasses, and OpenAI function schemas are generated directly from your real JSON — accurate field names, correct types, no guesswork.
Control LLM API costs before you scale
Token counts and per-call cost estimates for GPT-4o, Claude 3.5, and Gemini let you optimise prompt size and model choice before a feature goes to production.
Ready-to-run code and configs, not boilerplate
Every tool produces output you can paste directly into your project — API request code, prompt templates, chunk configurations — adapt rather than write from scratch.
Purpose-built for AI engineering workflows
From RAG chunking strategy to prompt template selection to function calling schema design — tools built for the specific problems AI engineers encounter daily.
Frequently asked questions
What happens if my prompt exceeds the context window?
The API returns an error (e.g. 'context_length_exceeded' for OpenAI). Some libraries truncate the input automatically, which can cause the model to lose the beginning of the conversation or important context. Always check your token count before sending.
Do output tokens count against the context window?
Yes — the context window limit applies to input + output tokens combined. If your model has a 128k context and your prompt is 120k tokens, the maximum response length is 8k tokens, not 128k.
Why is my token estimate different from the exact count?
This tool uses a character-based approximation (chars ÷ 4) which works well for English prose. Code, numbers, and non-Latin scripts tokenise differently. Use the Prompt Token Counter for tiktoken-accurate counts.
How do I reduce my context usage?
Summarise long conversation history instead of sending full turns. Compress system prompts. Use RAG to retrieve only relevant document chunks instead of the full document. Remove redundant examples from few-shot prompts.
What is a 'million token context' useful for?
Gemini 1.5 Pro's 2M context can hold entire codebases, books, or legal document sets. Instead of chunking and retrieving (RAG), you can send everything at once and let the model find what's relevant — simpler architecture at higher per-request cost.